MSTAR object classification and confuser and clutter rejection using Minace filters

Abstract

This paper presents the status of our SAR automatic target recognition (ATR) work on the Moving and Stationary Target Acquisition and Recognition (MSTAR) public database using the minimum noise and correlation energy (MINACE) distortion-invariant filter (DIF). We use a subset of the MSTAR public database for the benchmark three-class problem and we address confuser and clutter rejection. To handle the full 360° range of aspect view in MSTAR data, we use a set of Minace filters for each object; each filter should recognize the object (and its variants) in some angular range. We use fewer DIFs per object than prior work did. The Minace parameter c trades-off distortion-tolerance (recognition) versus discrimination (confuser/clutter rejection) performance. Our filter synthesis algorithm automatically selects the Minace filter parameter c and selects the training set images to be included in the filter, so that the filter can achieve both good recognition and good confuser and clutter rejection performance; this is achieved using a training and validation set. In our new filter synthesis method, no confuser, clutter, or test set data are used. The peak-to-correlation energy (PCE) ratio is used as the correlation plane metric in both filter synthesis and in tests, since it works better than correlation peak height. In tests, we do not assume that the test input's pose is known (as most prior work does), since pose estimation of SAR objects has a large margin of error; we describe our procedure for proper use of pose estimates in MSTAR recognition. The use of circular versus linear correlations is addressed. We also address the use of multi-look SAR data to improve performance.

Automated distortion-invariant filter synthesis and training set selection (auto-Minace)

“…5; the face and IR databases are described in Sect. 4. Performance results for SAR ATR are explained in Sect.…”

Confuser rejection performance of EMACH filters for MSTAR ATR

“…This is poorer than one would expect, since a number of the clutter chips were used in filter synthesis and β is chosen for clutter rejection. Other work using other distortion invariant filters (the MINACE filter [7]) obtained P CFA ≈0.5%, at the same operating point (i.e. the EER point).…”

“…Both results are very poor. Prior work using the MINACE filter [7], the Feature Space Trajectory (FST) [8] and the Support Vector and Discrimination Machine [9] (SVRDM) gave much better EERs of 13%, 17% and 23% .…”

Sparse Representation-Based SAR Image Target Classification on the 10-Class MSTAR Data Set

“…Simultaneously, a better classification performance of SRC over SVM has been verified. It is noticeable that the MINACE filter and SVM are used in recognition of the three-class targets, respectively [3,5]. Then, the two methods are extended to the recognition of the 10-class targets [15,16].…”

“…So the addition of an object requires creating an additional set of templates, thus causing burdensome calculation. To improve the performance, a number of methods called correlation pattern recognition have been presented [3], and they accomplish the training of the filter through minimizing the correlation between the filter and the spectral envelope of the training set in the frequency domain. Beyond that, a Learning Vector Quantization (LVQ) has been used for learning the templates for classification in [4].…”